3 Advances Changing the Future of Artificial Intelligence in Manufacturing

Today production lines are easy to envision as futuristic-seeming hives of automation, where industrial robots mimic the movements and, seemingly, the purposefully of human workers. Today s robots are not only working faster and more reliably than their human partners but also performing tasks ahead of human capability in total, such as microscopically accurate assembly. However, numerous of those robots are dumber than they look. That is, they may be more dexterous than humans, but they are programmed to function a constrained variety of tasks. Many robots cannot securely work in close vicinity to humans and actually have to be caged or regulated in ways that safeguard human coworkers. Artificial intelligence AI is just now finding its niche in manufacturing, as the technology matures and costs drop and as manufacturers discover applications for which AI algorithms can make complex decisions.

The Rise of AI in New Manufacturing Markets In manufacturing, capital investments are high and profit margins are often thin. Those conditions helped to drive a lot of manufacturing to low-wage countries, where the human-resource costs have been so low that the capital investment in AI and related automation was hard to justify. However, rising living standards and wages in places like India have made AI an easier sell. In fact, China is already making significant investments in AI for manufacturing and e-commerce. In addition, just as US, workers have lamented loss of jobs to automation; the same is now occurrence in Chinese factories. Although robots will replace many workers in the short term, the end game will be to retrain those specialists to function higher-level design, programming, or maintenance tasks. The real driver, however, will be to build up applications for AI in manufacturing so as to don t just robotize everyday jobs, but build completely new-fangled business processes feasible.

Machine vision is one of these applications. Formulating cameras many times more sensitive than the human eye has been the easy part. What AI adds is the progressively convenient capability to make sense of the images. Landing.ai, a startup formed by Silicon Valley veteran Andrew Ng, focuses on manufacturing problems such as precise quality analysis. It has developed machine-vision tools to find microscopic defects in items such as circuit boards at resolutions well beyond human vision, using a machine-learning algorithm trained on remarkably small volumes of sample images. That is a micro level challenge. A macro level problem is training a robot to sense what is going on around it so that it can avoid disruptions or danger. This is analogous to the self-driving-vehicle problem, which is nearing real-world adoption. There is a likely role in factories for smart, self-driving forklifts and conveyors to move materials and finished goods around.

AI in the Manufacturing Supply Chain and Beyond AI certainly is making robots more capable and simpler for humans to collaborate with. However, it will have an influence in areas that have nil to do with robotics. In the supply chain, for example, algorithms can observe designs of demand for products across time, geographic markets, and socioeconomic segments while accounting for macroeconomic cycles, political developments, and even weather patterns. The output can be a forecast of market demand, which in turn could drive raw material sourcing, human staffing, financing decisions, stock, maintenance of gear, and energy consumption. In manufacturing, AI is also increasingly important in predictive maintenance for equipment, with sensors tracking operating conditions and performance of factory tooling, learning to predict breakdowns and glitches, and taking or recommending preemptive actions.